Modern consumers spend a considerable amount of time online conducting various activities, including without limitation purchasing products and services, planning travel, getting news, researching medical information, participating in social networking, etc. Various websites, including e-commerce websites, social networking websites, etc., collect a large amount of data about a user and his or her online behavior for data mining purposes.
Examples of information collected by social network websites, often represented as a “social graph,” include a user's name, age, gender, education, interests, likes/dislikes, locations, etc. Vendors and online service providers apply various data mining techniques including without limitation statistical analysis, to the collected data to find patterns and relationships. Vendors can use the collected data and the associated patterns and relationships to inform targeted advertisements and viral marketing campaigns, to enhance a user's online experience, etc. For example, targeted marketing may involve providing recommendations to the users, such as recommended products, music, books, travel destinations, etc. However, such recommendations are generally so called “black box” recommendations in that they are generated based on predictions of a user's interests computed from the collected data and the patterns and relationships detected within it. Accordingly, in many circumstances, such black box recommendations are off-target and do not align with a user's actual interests, especially at the time of a particular interaction with the vendor's website.